Cohort Analysis
Analyze user groups ("cohorts") to identify behavioral trends and predict future actions based on shared experiences.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add cohort-analysis npx -- -y @trustedskills/cohort-analysis
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"cohort-analysis": {
"command": "npx",
"args": [
"-y",
"@trustedskills/cohort-analysis"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill enables AI agents to perform cohort analysis, a technique for tracking groups of users (cohorts) with shared characteristics over time. It allows you to identify behavioral trends, measure retention rates and churn, and predict future actions based on these shared experiences. The skill uses Python code internally to process user lifecycle data and generate insights about cohorts.
When to use it
- Measuring user retention rates and identifying when users churn.
- Analyzing customer lifetime value (LTV) and payback periods.
- Comparing performance across different user acquisition channels or campaigns.
- Understanding how product changes affect different user groups over time.
- Tracking engagement patterns and identifying early warning signs of churn.
Key capabilities
- Cohort Definition: Groups users based on shared characteristics like signup date, behavior, revenue, location, or demographics.
- Retention Rate Calculation: Determines the percentage of a cohort that remains active over time.
- Churn Rate Analysis: Identifies the percentage of a cohort that stops using a product or service.
- Cohort Age Tracking: Monitors how cohorts degrade over time (e.g., monthly).
- Data Visualization: Generates visualizations (using matplotlib and seaborn) to represent retention curves and other cohort-related metrics.
Example prompts
- "Analyze users who signed up in January 2023 and show me their retention rate over the past six months."
- "Compare the lifetime value of users acquired through Facebook ads versus those from organic search."
- "Show me how product feature X impacted user engagement for cohorts after its release."
Tips & gotchas
- The skill requires Python to be installed and accessible within the AI agent's environment.
- The provided source code generates sample data; you will need to provide your own user lifecycle data for real-world analysis.
- Understanding core concepts like "cohort size," "retention curve," and "churn rate" is helpful for interpreting results.
Tags
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